US11158030B2 - Noise reduction in computed tomography data - Google Patents

Noise reduction in computed tomography data Download PDF

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US11158030B2
US11158030B2 US16/808,354 US202016808354A US11158030B2 US 11158030 B2 US11158030 B2 US 11158030B2 US 202016808354 A US202016808354 A US 202016808354A US 11158030 B2 US11158030 B2 US 11158030B2
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Michael Manhart
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Siemens Healthineers AG
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    • G06T5/70—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/001—Image restoration
    • G06T5/002—Denoising; Smoothing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration by the use of local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20028—Bilateral filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20036—Morphological image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20172—Image enhancement details
    • G06T2207/20192—Edge enhancement; Edge preservation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20224—Image subtraction

Definitions

  • the present embodiments relate to noise reduction in a three-dimensional computed tomography dataset.
  • Computed tomography has now become established, especially as a medical imaging modality.
  • computed tomography devices in which at least parts of the recording arrangement consisting of x-ray emitter and x-ray detector are moved within a gantry, it has been proposed ever more frequently that x-ray devices having flexibly-positionable recording arrangements are also employed in order to record two-dimensional projection images of a patient from different directions (e.g., with different recording geometries) and to reconstruct three-dimensional computed tomography datasets therefrom (e.g., using Filtered Back Projection (FBP)).
  • FBP Filtered Back Projection
  • C-arm x-ray device e.g., an x-ray device with a C-arm
  • C-arm CT C-arm computed tomography
  • the image quality in soft tissue is limited, however.
  • One of the reasons for this limited image quality is a high level of noise in the slice images, which arises as a result of quantum and electron noise at the detector.
  • a good soft tissue contrast is important for many application cases (e.g., neuroradiological application cases such as the exclusion of bleeding after a neuroradiological procedure and in diagnosis of strokes).
  • neuroradiological application cases such as the exclusion of bleeding after a neuroradiological procedure and in diagnosis of strokes.
  • a differentiation between gray and white tissue in the brain is of importance for determining the size of the infarction.
  • the present embodiments may obviate one or more of the drawbacks or limitations in the related art. For example, a possibility for noise reduction in computed tomography datasets that obtains high-contrast details and enhances the soft tissue contrast is provided.
  • a first edge-preserving filter is applied to the reconstructed computed tomography dataset to obtain a first intermediate dataset.
  • a morphological filter is applied to the reconstructed computed tomography dataset to obtain a second intermediate dataset.
  • a first weighting dataset weighting edges more strongly is established from a first subtraction dataset of the first intermediate dataset and the second intermediate dataset.
  • a noise-reduced result dataset is established as the weighted sum of the first intermediate dataset and the second intermediate dataset.
  • the first intermediate dataset is weighted with the first weighting dataset, and the second intermediate dataset is weighted with one minus the first weighting dataset.
  • the noise-reduced computed tomography dataset is established as the result dataset or from the result dataset.
  • a reduction in noise that is as good as possible may be achieved, while retaining the contrast in the bones and in the soft tissue. It is not sufficient just to apply a filter to the reconstructed computed tomography dataset, since these types of noise-reducing filter are usually developed for photographs. By contrast with photographs, there are far greater dynamic ranges present in computed tomography datasets that are the result of very high contrast on bones with, at the same time, low contrasts and high noise in soft tissue. Therefore, the direct application of non-linear filters, which were originally developed for noise reduction in photographs, does not give the best possible results.
  • edge-preserving non-linear filters to a reconstructed computed tomography dataset, although leading to a greater spatial resolution, also provides that the image quality may deteriorate (e.g., since image artifacts (usually not occurring in photographs) may be additionally amplified and the like).
  • image quality may deteriorate (e.g., since image artifacts (usually not occurring in photographs) may be additionally amplified and the like).
  • image artifacts usually not occurring in photographs
  • the soft tissue contrast is not optimal, since finer, diagnostically relevant differences may be misinterpreted as noise.
  • a guided filter or a bilateral filter may be used as the edge-preserving filter, and/or a bitonic filter may be used as the morphological filter.
  • a combination of a guided filter and a bitonic filter may be provided.
  • Other morphological filters may include mean value filters and the like, for example.
  • the guided filter is described and defined, for example, in an article by Kaiming He et al., “Guided Image Filtering,” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (2013), pages 1397-1409.
  • the bitonic filter is described, for example, in an article by Graham Treece, “The Bitonic Filter: Linear Filtering in an Edge-Preserving Morphological Framework,” IEEE Transaction on Image Processing 25 (2016), pages 5199-5211.
  • the application of the guided filter on its own in computed tomography datasets, depending on parameterization leads to a limited edge preservation or to a good edge preservation, where in the latter case, however, a “grainy” impression arises in the soft tissue, in which individual edges caused by streak artifacts appear.
  • an exemplary embodiment of the provides a combination of the guided filter, parameterized for good edge preservation, with the bitonic filter. This also, for example, opens up the possibility of an implementation in a computationally efficient post-processing step.
  • non-linear filters of one or more of the present embodiments based on computed tomography, which is also referred to as “morphological filter with edge preservation” (e.g., “bitonic filter with edge preservation”), leads to a marked reduction of the noise level, a preservation of high-contrast details, and outstanding soft tissue contrast.
  • morphological filter with edge preservation e.g., “bitonic filter with edge preservation”
  • the bitonic filter represents a combination of morphological operators and a Gauss filter.
  • morphological opening and closing operations for example, are applied to the initial image, where the output is a weighted sum of the opening and closing, and weights are formed by Gauss-smoothed differential images between opening and original and closing and original.
  • the bitonic filter may be implemented quickly and easily and only has one significant parameter (e.g., the scope of the morphological subfilter).
  • the filter may be applied two-dimensionally to consecutive slice images in one image direction of the reconstructed computed tomography dataset.
  • the three-dimensional, reconstructed computed tomography dataset may be characterized by voxels defined with regard to a three-dimensional coordinate system, so that the three-dimensional, reconstructed computed tomography dataset may also be understood as a stack of slice images or sectional images.
  • the projection images are recorded along a circular path, there may be provision, for example, for the x and the y direction to lie in the plane of the circle and for the z direction, then also referred to as the axial direction, to lie at right angles hereto.
  • a one-dimensional filtering in the image direction to be applied to the result dataset of the two-dimensional filtering (e.g., the x-z slice images thus in the z direction (axial direction)).
  • the result dataset of the two-dimensional filtering e.g., the x-z slice images thus in the z direction (axial direction)
  • the filtering in the image direction a further visible reduction of the noise in the soft tissue is achieved.
  • bone structures are also preserved in this regard.
  • the use of the edge-preserving filter may be dispensed with for the filtering in the axial direction, so that for one-dimensional filtering, a one-dimensional filter (e.g., corresponding to the second, morphological filter; a bitonic filter) is applied to the result dataset for obtaining a third intermediate dataset, a second weighting dataset weighting edges more strongly is established from the subtraction dataset of the third intermediate dataset and the result dataset, and the noise-reduced computed tomography dataset is established as the weighted sum of the result dataset and the third intermediate dataset.
  • the result dataset is weighted with the second weighting dataset
  • the third intermediate dataset is weighted with one minus the second weighting dataset. In this case, more strongly weighting provides that edges are weighted more strongly by comparison with other image contents.
  • the corresponding weight of the weighting dataset is set to zero.
  • the corresponding weight of the weighting dataset is set to one. Otherwise, a weight between zero and one is selected for a constant transition (e.g., as the quotient of the amount of the distance between the subtraction data item and the minimum subtraction value and the amount of the distance between the maximum and the minimum subtraction value).
  • the maximum and the minimum subtraction value may be derived from the known imaging characteristics of different tissue types, but also in addition and/or as an alternative, may be determined heuristically (e.g., by evaluation of existing image impressions by users).
  • the minimum and the maximum subtraction value thus represent threshold values, which define a transition area that distinguishes between strong contrast differences resulting from different edge information and smaller differences in the low-contrast area. It is provided in this way that the high-contrast structures (e.g., bones) continue to be preserved in outstanding spatial resolution, weaker edges in the soft tissue also remain sufficiently present, and actual pure noise structures may disappear.
  • the minimum and maximum subtraction values may be selected differently for the first and the second weighting dataset.
  • the present embodiments may be employed to particular advantage in the area of neuroradiology.
  • the invention also relates to an x-ray device (e.g., a C-arm x-ray device) that has a control device embodied for carrying out the method of one or more of the present embodiments.
  • a noise reduction may be employed directly at the x-ray device itself (e.g., after selection by a user) in a processing step able to be implemented easily and computationally-efficiently.
  • This may be provided, for example, for assessing the computed tomography dataset directly at the x-ray device and/or when using the x-ray device as an accompaniment to a medical intervention (e.g., a neurological intervention on the brain).
  • the C-arm x-ray device may thus have a C-arm, on which an x-ray emitter and an x-ray detector are arranged opposite one another.
  • the arrangement formed by the x-ray emitter and the x-ray detector may be positioned for specific recording geometries.
  • the control device which may have at least one processor and at least one storage device, may also already be embodied for reconstruction of the computed tomography dataset from the two-dimensional projection images.
  • a control device may thus have a reconstruction unit for establishing the reconstructed computed tomography dataset, a first filter unit and a second filter unit for use of the first filter or the second filter, a weighting determination unit for establishing the first weighting dataset, and a weighting unit for establishing the noise-reduced result dataset.
  • Further functional units in accordance with embodiments may be provided.
  • a third filter unit for using the one-dimensional filter may be provided.
  • the weighting determination unit and the weighting unit may also be embodied to undertake the corresponding weightings in relation to the third intermediate dataset and the result dataset.
  • One or more (e.g., all) of the functional units may be formed by one or more processors.
  • a computer program of one or more of the present embodiments is able to be loaded directly into a memory of a computing device (e.g., with a control device of an x-ray device) and includes instructions for carrying out the acts of a method when the computer program is executed in the computing device.
  • the computer program may be stored on an electronically-readable data medium (e.g., a non-transitory computer-readable storage medium) that includes electronically-readable control information stored thereon.
  • the electronically-readable control information includes at least one computer program and is configured so as to carry out a method when the data medium is used in a computing device (e.g., a control device of an x-ray device).
  • the data medium may, for example, involve a non-transient data medium (e.g., a CD-ROM).
  • FIG. 1 shows a flowchart of an exemplary embodiment of a method
  • FIG. 2 shows an influence diagram for further explanation of the method in accordance with FIG. 1 ;
  • FIG. 3 shows one embodiment of an x-ray device
  • FIG. 4 shows an exemplary functional structure of a control device of the x-ray device in accordance with FIG. 3 .
  • FIG. 1 shows a flowchart for explaining an exemplary embodiment of a method.
  • act S 1 projection images of a head of a patient are recorded with a C-arm x-ray device using different recording geometries.
  • act S 2 within the control device of the x-ray device, the reconstruction of a three-dimensional, reconstructed computed tomography dataset takes place (e.g., by filtered back projection). The noise in this is to be reduced while retaining all information of importance for diagnosis.
  • a first non-linear edge-preserving filter e.g., a guided filter G
  • a second, non-linear, morphological filter e.g., of a bitonic filter B
  • W e.g., stack of weighting images
  • W ( x,y,z ) 0 for all
  • W ( x,y,z ) (
  • ⁇ d min )/( d max ⁇ d min ) for all d min ⁇
  • ⁇ d max
  • W ( x,y,z ) 1 for all
  • the noise is also reduced by a filtering in the image direction (e.g., via the axial direction (z direction) after the image slices were defined in the x-y plane).
  • FIG. 2 shows these relationships once again more precisely.
  • the starting point as described, is the reconstructed computed tomography dataset F expressed as a stack of image slices, which follow on from each other in the image direction.
  • the intermediate datasets Z1 and Z2 stem.
  • the first subtraction dataset D is produced from this. This is used, as described for act S 5 , in order to determine the first weighting dataset G.
  • the result dataset E is produced by weighted addition 2 of the intermediate datasets Z1 and Z2 using the weighting dataset G.
  • the one-dimensional bitonic filter B1 is then applied to these in the image direction in order to obtain the third intermediate dataset Z3.
  • the second subtraction dataset DZ is established, which is used in a similar way to act S 5 in order to obtain the second weighting dataset WZ.
  • FIG. 3 shows a basic diagram of one embodiment of an x-ray device 5 (e.g., a C-arm x-ray device) that thus includes a C-arm 7 supported on a stand 6 , which may be mobile, on the ends of which an x-ray emitter 8 and an x-ray detector 9 are arranged opposite one another as a recording arrangement.
  • the recording arrangement may be brought into different recording geometries as regards a patient supported on a patient table 10 .
  • control device 11 which is also embodied to carry out the method of one or more of the present embodiments.
  • FIG. 4 shows the functional layout of the control device 11 more precisely.
  • the control device 11 also includes a reconstruction unit 13 for reconstruction of three-dimensional computed tomography datasets from projection images.
  • a first filter unit 14 and a second filter unit 15 the two-dimensional first and second filters are applied in accordance with acts S 3 and S 4 .
  • the results, the first and the second intermediate dataset, are then passed on to a weight determination unit 16 in order to determine the first weighting dataset in accordance with act S 5 .
  • a weighting unit 17 establishes the result dataset in accordance with act S 6 .
  • a further filter unit 18 may also be provided for carrying out act S 7 ; the acts S 8 and S 9 may be carried out in turn by the weight determination unit 16 and the weighting unit 17 , so that at the end, the noise-reduced computed tomography dataset is obtained. If, incidentally, one-dimensional filtering is to be dispensed with, the noise-reduced computed tomography dataset is produced directly as the result dataset.

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